The Immunology Team, which is now part of the Lymphocyte Biology Section, Laboratory of Immune System Biology, reported previously on a top-down analysis of the immune and non-immune tissue response of mice to various strains of influenza virus. Highly standardized preparations of both mildly pathogenic (Tx91) and highly pathogenic (PR8) viruses were used at varying infectious doses in a single inbred strain of mouse and several hundred highly qualified microarray transcriptional analyses conducted with RNA isolated from infected lung tissues of these mice at varying time points post-inoculation. Our analysis of these data uncovered a positive feedback pathway involving virus-induced chemokine production facilitates recruitment of myeloid cells to the lungs. The implication was that uninterrupted amplification of myeloid cell recruitment and inflammatory cytokine production can play a key role in pathogenic infections. In support of this model, attenuating but not eliminating myeloid cell recruitment using depleting antibodies rescues mice from early lethality of PR8 infection. Thus, this study uncovered a core feedback circuit involving innate inflammation that drives early lethality in influenza infection and provides new targets for intervention in this disease. We are now extending this work in an effort to develop a robust computational framework for utilizing peripheral blood transcriptomic data to define tissue-specific pathology. To date, this has proven very difficult, in large measure because the multiple testing correction needed when using whole genome transcriptional data. We tested the hypothesis that a tissue-based definition of a lethal signature can be used to reduce the effects of such multiple testing and hence reveal weak signals in blood-based data sets. Using mice infected with lethal PR8 virus, we determined that our prior lung signature could be seen in blood cells of infected animals and designed a Fluidigm panel based on these data to test whether we could predict which animals would die when a group was infected at one LD50. Interestingly, while the signature gene transcripts were detected only in mice infected with PR8 and not the non-lethal H1N1 virus Tx91, and all animals showed evidence of infection from weight loss, we could not discriminate among PR8 infected animals in terms of which would die. This suggest two possibilities. Slight differences in the actual numbers of infectious particles given each animal dictate whether they survive or not in a manner that early blood signatures cannot reveal, or more interestingly, the stochastic variation among even inbred mice in their adaptive immune repertoires might dictate who suppresses potentially lethal innate inflammation before terminal pulmonary compromise. Because the animals cannot be sacrificed for conventional plaque assays, we must rely on indirect measures to assess whether there is an initial difference in the infectious dose that reads out as differential lethality. Neither early weight loss nor the intensity of transcriptional signals for gene that are common to influenza infected mice correlated with early differences in viral load. The alternative models are that either epigenetic variations that are not stringently inherited or differences in adaptive immune repertoire among highly inbred animals accounts for the different outcomes. The latter possibility of repertoire variation is consistent with initial data showing that survival is predictable from the level of signal obtained with probes that correspond to the number of effector CD8 T cell circulating on day 5 and 6 after infection. We are currently testing this model using cell depletion studies. The outcome of these experiments has potentially important implications for understanding why some humans die from potentially lethal infections and other survive, as well as suggesting possible interventions that can tip the balance. A second project involves use of the emerging tools of systems biology to investigate the unexplored roles of many NLRs. In the course of such study, Dr. Subramanian (now leading her own laboratory at the Institute for Systems Biology) has observed profound effects of very small changes in intracellular protein concentration on signaling through the NOD1 pathway. Under normal conditions, several miRNAs contribute to maintaining expression of NOD1 below the level leading to ligand-independent gene activation. Alteration in expression of these miRNAs is linked to an increases severity of gastric cancer, which was previously linked to NOD1. These data may be of importance in understanding how small eQTLs linked to inflammatory and autoimmune diseases operate to cause pathology. Based on these findings, we are exploring in various experimental systems whether small (1-2 fold changes in gene expression, as seen with many eQTLs), can lead to disease by imbalancing activation and negative regulatory pathways. We suspect this type of dysregulation might contribute to various autoimmune states. As part of the larger LSB group effort to better understand TLR signaling in macrophages, we have conducted fine grained time and dose studies at the single cell level and on bulk populations looking at a diverse set of downstream signaling events and effector responses. We previously reported quantitative data suggesting that the macrophage response system has evolved to limit potentially damaging inflammation in the face of minor pulses of PAMP or DAMP signals engaging TLR (the steady-state), but to prepare for anti-pathogen responses under such conditions in case these weak signals are not from commensals or normal tissue turnover but from an incipient infection. Remarkably, both humans and various mouse strains all share nearly identical dose-responses and among random human donors from the Blood Bank, the responses are nearly identical, a very unusual result in comparison to data on normal volunteers in the CHI influenza and other studies. The data from this project are also serving to drive development of new Simmune-based signaling models for the TLR pathway. In a major extension of this macrophage signaling project, we conducted related studies using graded combinations of TLR inputs to develop an understanding of how macrophages decode the multiple PAMP signals of pathogens. The resulting dual dose response matrices showed what we term hot spots in which the combination of less than maximal concentrations of a TLR 2 and TLR4 ligand gave higher cytokine output after several hours than even a higher dose of either ligand alone. We initially thought this presented positive synergy, but further analysis revealed that the results represented the more rapid extinction of cytokine production with high single ligand doses than with the lower combined challenges. In particular, high doses of TLR4 ligand caused this early cessation of response. Transcriptional profiling led to identification of a small number of candidate regulators, most described in the literature as having a negative influence on gene expression. Further work showed that the hot spot pattern predicted the capacity of macrophages to discriminate Gram+ from Gram organisms and that again, the Gram-, with their better capacity to trigger TLR4 signaling, showed rapid cytokine shutdown. Recent work identified a type 1 interferon feedback circuit as responsible for this inhibitory effect, which is independent of the known IL-10 dependent regulatory circuit. These data provide novel insights into the role of type 1 interferon in bacterial infections and also evidence for the crucial role of feedback inhibition in allowing these innate immune cells to discriminate two major classes of pathogens.